bert-3-epoch-sentiment

This model is a fine-tuned version of bert-base-uncased on the tweet_eval dataset. It achieves the following results on the evaluation set:

• Loss: 2.5772
• Accuracy: 0.6746
• Precision: 0.6776
• Recall: 0.6746
• Micro-avg-recall: 0.6746
• Micro-avg-precision: 0.6746

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Micro-avg-recall	Micro-avg-precision
0.1114	1.0	2851	2.2420	0.6662	0.6705	0.6662	0.6662	0.6662
0.0949	2.0	5702	2.5923	0.6539	0.6676	0.6539	0.6539	0.6539
0.2172	3.0	8553	2.5772	0.6746	0.6776	0.6746	0.6746	0.6746

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3

Author

Priyanka Balivada